




'''simple docstring'''







from __future__ import annotations

lowerCAmelCase_     						=				list[tuple[int, int]]

lowerCAmelCase_     						=				[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

lowerCAmelCase_     						=				([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right

class 	_snake_case:






 def __init__(self       :  List[Any]    ,		a       :  int    ,		a       :  int    ,		a       :  int    ,		a       :  int    ,		a       :  float    ,		a       :  Node | None    ,		)       ->   Optional[int]:






    """simple docstring"""


    A__       =     pos_x
    A__       =     pos_y
    A__       =     (pos_y, pos_x)
    A__       =     goal_x
    A__       =     goal_y
    A__       =     g_cost
    A__       =     parent
    A__       =     self.calculate_heuristic()






 def 						_UpperCamelCase (self       :  int    )       ->   float:






    """simple docstring"""


    A__       =     abs(self.pos_x - self.goal_x    )
    A__       =     abs(self.pos_y - self.goal_y    )
    return dx + dy






 def __lt__(self       :  Optional[int]    ,		a       :  Any    )       ->   bool:






    """simple docstring"""


    return self.f_cost < other.f_cost




class 	_snake_case:






 def __init__(self       :  List[str]    ,		a       :  tuple[int, int]    ,		a       :  tuple[int, int]    )       ->   Dict:






    """simple docstring"""


    A__       =     Node(start[1]    ,		start[0]    ,		goal[1]    ,		goal[0]    ,		0    ,		a    )
    A__       =     Node(goal[1]    ,		goal[0]    ,		goal[1]    ,		goal[0]    ,		9_99_99    ,		a    )

    A__       =     [self.start]
    A__       =     []

    A__       =     False






 def 						_UpperCamelCase (self       :  List[Any]    )       ->   Path | None:






    """simple docstring"""


    while self.open_nodes:
       # Open Nodes are sorted using __lt__
       self.open_nodes.sort()
       A__       =     self.open_nodes.pop(0    )

       if current_node.pos == self.target.pos:
          A__       =     True
          return self.retrace_path(a    )

       self.closed_nodes.append(a    )
       A__       =     self.get_successors(a    )

       for child_node in successors:
          if child_node in self.closed_nodes:
             continue

          if child_node not in self.open_nodes:
             self.open_nodes.append(a    )
          else:
             # retrieve the best current path
             A__       =     self.open_nodes.pop(self.open_nodes.index(a    )    )

             if child_node.g_cost < better_node.g_cost:
                self.open_nodes.append(a    )
             else:
                self.open_nodes.append(a    )

    if not self.reached:
       return [self.start.pos]
    return None






 def 						_UpperCamelCase (self       :  Any    ,		a       :  Node    )       ->   list[Node]:






    """simple docstring"""


    A__       =     []
    for action in delta:
       A__       =     parent.pos_x + action[1]
       A__       =     parent.pos_y + action[0]

       if not (0 <= pos_x <= len(grid[0]    ) - 1 and 0 <= pos_y <= len(a    ) - 1):
          continue

       if grid[pos_y][pos_x] != 0:
          continue

       successors.append(
           Node(
               a    ,		a    ,		self.target.pos_y    ,		self.target.pos_x    ,		parent.g_cost + 1    ,		a    ,		)    )
    return successors






 def 						_UpperCamelCase (self       :  Optional[int]    ,		a       :  Node | None    )       ->   Path:






    """simple docstring"""


    A__       =     node
    A__       =     []
    while current_node is not None:
       path.append((current_node.pos_y, current_node.pos_x)    )
       A__       =     current_node.parent
    path.reverse()
    return path


if __name__ == "__main__":
    lowerCAmelCase_     						=				(0, 0)
    lowerCAmelCase_     						=				(len(grid) - 1, len(grid[0]) - 1)
    for elem in grid:
        print(elem)

    print('''------''')

    lowerCAmelCase_     						=				GreedyBestFirst(init, goal)
    lowerCAmelCase_     						=				greedy_bf.search()
    if path:
        for pos_x, pos_y in path:
            lowerCAmelCase_     						=				2

        for elem in grid:
            print(elem)








'''simple docstring'''







from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


lowerCAmelCase_     						=				logging.get_logger(__name__)  # pylint: disable=invalid-name







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   if not path:
      return "pipe"

   for ext in PipelineDataFormat.SUPPORTED_FORMATS:
      if path.endswith(UpperCAmelCase						):
         return ext

   raise Exception(
       F"""Unable to determine file format from file extension {path}. """
       F"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"""						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     pipeline(
       task=args.task      ,model=args.model if args.model else None      ,config=args.config      ,tokenizer=args.tokenizer      ,device=args.device      ,)
   A__       =     try_infer_format_from_ext(args.input						) if args.format == 'infer' else args.format
   A__       =     PipelineDataFormat.from_str(
       format=UpperCAmelCase      ,output_path=args.output      ,input_path=args.input      ,column=args.column if args.column else nlp.default_input_names      ,overwrite=args.overwrite      ,)
   return RunCommand(UpperCAmelCase      ,UpperCAmelCase						)







class 	_snake_case(			UpperCAmelCase      ):






 def __init__(self       :  Tuple    ,		a       :  Pipeline    ,		a       :  PipelineDataFormat    )       ->   Union[str, Any]:






    """simple docstring"""


    A__       =     nlp
    A__       =     reader






 @staticmethod
 def 						_UpperCamelCase (a       :  ArgumentParser    )       ->   str:






    """simple docstring"""


    A__       =     parser.add_parser('run'    ,		help='Run a pipeline through the CLI'    )
    run_parser.add_argument('--task'    ,		choices=get_supported_tasks()    ,		help='Task to run'    )
    run_parser.add_argument('--input'    ,		type=a    ,		help='Path to the file to use for inference'    )
    run_parser.add_argument('--output'    ,		type=a    ,		help='Path to the file that will be used post to write results.'    )
    run_parser.add_argument('--model'    ,		type=a    ,		help='Name or path to the model to instantiate.'    )
    run_parser.add_argument('--config'    ,		type=a    ,		help='Name or path to the model\'s config to instantiate.'    )
    run_parser.add_argument(
        '--tokenizer'    ,		type=a    ,		help='Name of the tokenizer to use. (default: same as the model name)'    )
    run_parser.add_argument(
        '--column'    ,		type=a    ,		help='Name of the column to use as input. (For multi columns input as QA use column1,columns2)'    ,		)
    run_parser.add_argument(
        '--format'    ,		type=a    ,		default='infer'    ,		choices=PipelineDataFormat.SUPPORTED_FORMATS    ,		help='Input format to read from'    ,		)
    run_parser.add_argument(
        '--device'    ,		type=a    ,		default=-1    ,		help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'    ,		)
    run_parser.add_argument('--overwrite'    ,		action='store_true'    ,		help='Allow overwriting the output file.'    )
    run_parser.set_defaults(func=a    )






 def 						_UpperCamelCase (self       :  int    )       ->   Optional[Any]:






    """simple docstring"""


    A__  ,			A__       =     self._nlp, []

    for entry in self._reader:
       A__       =     nlp(**a    ) if self._reader.is_multi_columns else nlp(a    )
       if isinstance(a    ,		a    ):
          outputs.append(a    )
       else:
          outputs += output

        # Saving data
    if self._nlp.binary_output:
       A__       =     self._reader.save_binary(a    )
       logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""    )
    else:
       self._reader.save(a    )


